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liguoxuanliguoxuan
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migrate to mmdetection
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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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# cython generated cpp
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mmdet/ops/nms/*.cpp
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mmdet/version.py
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data
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.vscode
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.idea
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work_dirs/

‎.gitmodules

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[submodule "mmdetection"]
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path = mmdetection
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url = https://github.com/open-mmlab/mmdetection.git

‎compile.sh

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#!/usr/bin/env bash
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PYTHON=${PYTHON:-"python"}
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echo "Building roi align op..."
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cd mmdet/ops/roi_align
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if [ -d "build" ]; then
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rm -r build
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fi
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$PYTHON setup.py build_ext --inplace
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echo "Building roi pool op..."
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cd ../roi_pool
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if [ -d "build" ]; then
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rm -r build
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fi
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$PYTHON setup.py build_ext --inplace
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echo "Building nms op..."
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cd ../nms
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make clean
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make PYTHON=${PYTHON}
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echo "Building dcn..."
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cd ../dcn
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if [ -d "build" ]; then
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rm -r build
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fi
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$PYTHON setup.py build_ext --inplace
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echo "Building corner pooling..."
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cd ../_cpools
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if [ -d "build" ]; then
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rm -r build
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fi
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$PYTHON setup.py build_ext --inplace

‎configs/centripetalnet_mask_hg104.py

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#model settings
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model = dict(
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type='CentripetalNet',
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backbone=dict(
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type='Hourglass',
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n=5,
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nstack=2,
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dims=[256, 256, 384, 384, 384, 512],
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modules=[2, 2, 2, 2, 2, 4],
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out_dim=80,),
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neck=None,
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bbox_head=dict(
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type='Centripetal_mask',
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num_classes=81,
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in_channels=256,
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with_mask=False,
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))
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# training and testing settings
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train_cfg = dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.4,
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min_pos_iou=0,
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ignore_iof_thr=-1),
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smoothl1_beta=0.11,
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gamma=2.0,
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alpha=0.25,
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allowed_border=-1,
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pos_weight=-1,
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debug=False)
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test_cfg = dict(
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nms_pre=1000,
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min_bbox_size=0,
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score_thr=0.05,
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nms=dict(type='nms', iou_thr=0.5),
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max_per_img=100)
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# dataset settings
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dataset_type = 'CocoDataset'
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#data_root = 'data/mscoco2017/'
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data_root = '/mnt/lustre/share/DSK/datasets/mscoco2017/'
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img_norm_cfg = dict(
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mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
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cornernet_mode = True
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data = dict(
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imgs_per_gpu=6,#3
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workers_per_gpu=3,#3
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train=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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img_scale=(511, 511),
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img_norm_cfg=img_norm_cfg,
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size_divisor=None,
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flip_ratio=0.5,
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with_mask=True,
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with_crowd=False,
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with_label=True,
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resize_keep_ratio=False,
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cornernet_mode=cornernet_mode,
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extra_aug=dict(
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photo_metric_distortion=dict(
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brightness_delta=32,
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contrast_range=(0.5, 1.5),
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saturation_range=(0.5, 1.5),
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hue_delta=18),
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expand=dict(
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mean=img_norm_cfg['mean'],
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to_rgb=img_norm_cfg['to_rgb'],
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ratio_range=(1, 4)),
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random_crop=dict(
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min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3))),
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val=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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img_scale=(511, 511),
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img_norm_cfg=img_norm_cfg,
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size_divisor=None,
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flip_ratio=1,
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with_mask=False,
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with_crowd=False,
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with_label=True,
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cornernet_mode=cornernet_mode,
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resize_keep_ratio=False),
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test=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/image_info_test-dev2017.json',
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img_prefix=data_root + 'test2017/',
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img_scale=(511, 511),
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img_norm_cfg=img_norm_cfg,
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size_divisor=None,
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flip_ratio=1,
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with_mask=False,
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with_crowd=False,
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with_label=False,
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test_mode=True,
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cornernet_mode=cornernet_mode,
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resize_keep_ratio=False))
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# optimizer
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optimizer = dict(type='Adam', lr=0.00005)
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optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
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# learning policy
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# policy='fixed'
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lr_config = dict(
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policy='step',
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warmup='linear',
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warmup_iters=500,
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warmup_ratio=1.0 / 3,
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step=[190])
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checkpoint_config = dict(interval=1)
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# yapf:disable
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log_config = dict(
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interval=50,
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hooks=[
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dict(type='TextLoggerHook'),
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# dict(type='TensorboardLoggerHook')
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])
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# yapf:enable
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# runtime settings
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total_epochs = 210
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device_ids = range(8)
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dist_params = dict(backend='nccl')
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log_level = 'INFO'
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work_dir = './work_dirs/centripetalnet_mask_hg104'
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resume_from = None
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load_from = None
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workflow = [('train', 1)]
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‎init.sh

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#!/bin/bash
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PYTHON=${PYTHON:-"python"}
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# copy files
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cp compile.sh mmdetection/
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cp -r src/core/* mmdetection/mmdet/core/
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cp -r src/datasets/* mmdetection/mmdet/datasets/
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cp -r src/models/* mmdetection/mmdet/models/
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cp -r src/ops/* mmdetection/mmdet/ops/
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# compile and setup
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cd mmdetection
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./compile.sh
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$PYTHON setup.py install --user

‎mmdetection

Submodule mmdetection added at 6d83f89

‎src/core/__init__.py

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from .anchor import * # noqa: F401, F403
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from .bbox import * # noqa: F401, F403
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from .corner import *
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from .mask import *
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from .loss import * # noqa: F401, F403
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from .evaluation import * # noqa: F401, F403
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from .post_processing import * # noqa: F401, F403
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from .utils import * # noqa: F401, F403

‎src/core/corner/__init__.py

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from .corner_target import corner_target
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from .kp_utils import _gather_feat,_nms,_tranpose_and_gather_feat,_topk,_neg_loss,_sigmoid,_ae_loss,_regr_loss,gaussian2D,draw_gaussian,gaussian_radius, _decode_center
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__all__ = ['corner_target','_gather_feat','_nms','_tranpose_and_gather_feat','_topk','_decode_center','_neg_loss','_sigmoid','_ae_loss','_regr_loss','gaussian2D','draw_gaussian','gaussian_radius']
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